215 citations · 472 across the 17 of their papers we have counts for
6 papers · 1 filter
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
Soroosh Tayebi Arasteh, Leo Misera, Jakob Nikolas Kather +2
Pre-training datasets, like ImageNet, have become the gold standard in medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled d…
Using Multiple Dermoscopic Photographs of One Lesion Improves Melanoma Classification via Deep Learning: A Prognostic Diagnostic Accuracy Study
Achim Hekler, Roman C. Maron, Sarah Haggenmüller +28
Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the im…
Fibroglandular Tissue Segmentation in Breast MRI using Vision Transformers -- A multi-institutional evaluation
Gustav Müller-Franzes, Fritz Müller-Franzes, Luisa Huck +9
Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. I…
Diffusion Probabilistic Models beat GANs on Medical Images
Gustav Müller-Franzes, Jan Moritz Niehues, Firas Khader +8
The success of Deep Learning applications critically depends on the quality and scale of the underlying training data. Generative adversarial networks (GANs) can generate arbitrary…
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
Firas Khader, Gustav Mueller-Franzes, Soroosh Tayebi Arasteh +12
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input…
Image prediction of disease progression by style-based manifold extrapolation
Tianyu Han, Jakob Nikolas Kather, Federico Pedersoli +11
Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often…